Experience: 2-3 yearsAbout RiverfrontRiverfront is independent behavioral validation for AI agents. We test agents through their real interface, the way a user or regulator would, and produce evidence that boards, auditors, and regulators can rely on. Our methodology is aligned to the frameworks regulators are writing (EU AI Act, GDPR, UAE PDPL, Colorado AI Act, ISO/IEC 42001), and it's versioned end to end, so any result can be reproduced and defended months later.We work in a spec-driven, AI-orchestrated model where the quality of your thinking matters more than your typing speed. We're looking for people who can understand ambiguous problems, make sound technical decisions, and build.
Location: Pune, India/Dubai, UAEThe RoleYou will own complex problems from brief to delivery - understanding the problem, turning it into a precise spec, making architectural decisions, building alongside the team, and shipping with confidence.This is a hands-on engineering role with significant ownership.What You'll DoTurn complex product and client requirements into structured technical specs before development beginsArchitect and build agentic AI systems end to end - including RAG pipelines, multi-agent orchestration, MCP-based integrations, tool-use workflows, and memoryMake architecture decisions across model selection, retrieval strategy, memory design, and orchestration frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, and AutoGenOwn performance and cost trade-offs across token usage, latency, inference quality, and LLM providers including OpenAI, Anthropic, Vertex AI, Azure OpenAI, and AWS BedrockBuild observability and evaluation systems using tools such as Langfuse, LangSmith, Helicone, Arize, and W&BDefine evaluation criteria and determine what "good" actually means for an AI agentContribute directly to Riverfront's AI governance and evaluation platformParticipate in solution design, technical decisions, and product deliveryReview other developers' work and identify weak assumptions or shallow planning earlyJoin client and stakeholder conversations when needed and explain technical decisions clearlyHelp evolve the spec-driven development practices the team uses as we scaleWhat We're Looking ForYou have around 2-3 years of hands-on engineering experience, ideally in a product company, technical consultancy, startup, or agency where you have had real ownership.More importantly, you have shipped AI systems beyond the prototype stage.You should be comfortable with:Agentic and multi-agent AI systems with real tool use and memoryRAG pipelines and retrieval architectureVector databases such as Pinecone, ChromaDB, Weaviate, Qdrant, or MilvusBackend engineering - APIs, microservices, asynchronous systems, Docker/Kubernetes, and CI/CDModern LLM providers and model-selection trade-offsAI-assisted development using tools such as Claude Code or CursorYou have written specs or requirements documents, made architectural decisions, and stood behind them.
You are comfortable working directly with product leadership, clients, or other stakeholders rather than operating only from a ticket queue.You ask difficult questions before you start building and document important decisions as you go.This role is for someone who wants to stay deeply hands-on - building, experimenting, making technical decisions, and owning outcomes. It is not designed as a short path into people management. What You'll GetMeaningful ownership from day oneDirect involvement in product and technical decisionsWork on AI systems that are actually being deployed and evaluated in productionExposure across agentic AI, AI governance, evaluation, and enterprise AIThe opportunity to help shape both the product and the engineering practices behind itCompetitive compensation based on experience and location